mcpField
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Travel Itinerary Backend

Travel Itinerary Backend

Local
github.com/Shirisha-16Media & Design0Updated 78w ago

About Travel Itinerary Backend

Travel Itinerary Backend is a backend system for managing trips, including day-by-day lodging, transfers, and activities. It also provides itinerary recommendations based on the number of nights through MCP logic.

Key features

  1. 01Create and view trip itineraries
  2. 02Manage daily stays, transfers, and activities
  3. 03Recommend itineraries by number of nights
  4. 04Include sample data for Phuket and Krabi

Use cases

  1. 01Record day-by-day travel plans
  2. 02Get itinerary suggestions based on trip length

MCP support

The backend includes MCP recommendation logic that suggests itineraries based on the number of nights.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Travel Itinerary Backend

A backend system built with FastAPI and SQLAlchemy to manage travel itineraries, including hotel stays, transfers, activities, and day-wise plans. Also includes a recommendation endpoint via an MCP server.

Features

  • Create and view trip itineraries
  • Day-wise hotel accommodations, transfers, and activities
  • Recommended itineraries (MCP logic) based on number of nights
  • Sample data seeded for Phuket and Krabi

Tech Stack

  • FastAPI – API framework
  • SQLAlchemy – ORM for database modeling
  • SQLite – Lightweight database
  • Pydantic – Data validation

Getting Started

  1. Clone the repository

In bash git clone https://github.com/yourusername/travel-itinerary.git

cd travel-itinerary

2)Create Virtual Environment

python -m venv venv

Windows

venv\Scripts\activate

macOS/Linux

source venv/bin/activate

3)Install Dependencies

pip install -r requirements.txt

4)Run the server

uvicorn app.main:app --reload

5)Seed sample Data(optional)

python -m app.seed

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